Data Science with R - MA942

Location Term Level Credits (ECTS) Current Convenor 2019-20
Canterbury Autumn
View Timetable
7 15 (7.5)

Pre-requisites

MAST4009 (Probability), MAST4011 (Statistics), MAST4006 (Mathematical Methods 1), MAST4007 (Mathematical Methods 2), MAST5001 (Applied Statistical Modelling 1), and either MAST4004 (Linear Algebra) or MAST4005 (Linear Mathematics).

Restrictions

None

2019-20

Overview

Introduction: Machine learning and data visualisation with R.
Classification and prediction: Generalised linear model (GLM), linear discrimination analysis (LDA), k-nearest neighbors (KNN). R-based worked examples.
Resampling methods: Cross-validation (CV) and bootstrap. R-based worked examples.
Regression tree-based methods: Classification and regression trees (CART), bagging, random forests and boosting. R-based worked examples.
Support vector machines (SVM): Support vector classifier, regression SVM. R-based worked examples.
Machine Learning in Action:
(a) Biomedical and health data analysis;
(b) Bond default data analysis;
(c) Insurance data analysis;
(d) Financial data analysis;
(e) Other big data analysis.

Details

This module appears in:


Contact hours

44

Method of assessment

75% examination, 25% coursework

Indicative reading

Bishop, C. M. (2006), Pattern Recognition and Machine Learning. Springer, New York
James, G, Witten, D., Hastie, T., Tibshirani, R. (2013) Introduction to Statistical Learning. Springer, New York.
Sweeting, P. (2011) Financial Enterprise Risk Management. Cambridge University Press. Cambridge.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the module students will be able to:
1 demonstrate systematic understanding of the concepts involved in machine learning;
2 demonstrate the capability to solve complex problems using a high level of skill in calculation and manipulation of the material in the following areas: Supervised learning with R; data science for actuarial science, finance and other areas.
3 apply a range of concepts and principles in supervised learning in loosely defined contexts, showing good judgement in the selection and application of tools and techniques.

The intended generic learning outcomes. On successfully completing the module students will be able to:
1 work competently and independently, be aware of their own strengths and understand when help is needed;
2 demonstrate a high level of capability in developing and evaluating logical arguments;
3 communicate arguments confidently with the effective and accurate conveyance of conclusions;
4 manage their time and use their organisational skills to plan and implement efficient and effective modes of working;
5 solve problems relating to qualitative and quantitative information;
6 make effective use of information technology skills such as online resources (moodle);
7 communicate technical material effectively;
8 demonstrate an increased level of skill in numeracy and computation;
9 demonstrate the acquisition of the study skills needed for continuing professional development.

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